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Alex Beutel
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Publications
- 2023
- [c54]Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Ed H. Chi, Jilin Chen, Alex Beutel:
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. EMNLP (Findings) 2023: 127-139 - [i43]Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR abs/2302.11188 (2023) - [i41]Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Jilin Chen, Ed H. Chi, Alex Beutel:
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. CoRR abs/2305.13535 (2023) - 2022
- [i31]Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nick Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel:
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. CoRR abs/2210.07755 (2022) - 2021
- [c48]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES 2021: 873-883 - [c47]Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed H. Chi, Alex Beutel:
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? EMNLP (1) 2021: 4701-4712 - [c45]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. KDD 2021: 1748-1757 - [c44]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS 2021: 14358-14369 - [c43]Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. WSDM 2021: 436-444 - [c41]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. WWW 2021: 3872-3883 - [i28]Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Recommender System Effects with Simulated Users. CoRR abs/2101.04526 (2021) - [i26]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. CoRR abs/2105.02377 (2021) - [i25]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. CoRR abs/2105.09985 (2021) - [i24]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. CoRR abs/2106.02705 (2021) - 2020
- [c40]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. EMNLP (1) 2020: 5141-5146 - [c39]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS 2020 - [i22]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. CoRR abs/2006.13114 (2020) - [i21]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. CoRR abs/2006.16375 (2020) - [i20]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. CoRR abs/2010.02338 (2020) - [i17]Hussam Abu-Libdeh, Deniz Altinbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou Li, Andy Ly, Christopher Olston:
Learned Indexes for a Google-scale Disk-based Database. CoRR abs/2012.12501 (2020) - 2019
- [c37]Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel:
Counterfactual Fairness in Text Classification through Robustness. AIES 2019: 219-226 - [c36]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi:
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. AIES 2019: 453-459 - [c35]Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan:
SageDB: A Learned Database System. CIDR 2019 - [c34]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow:
Fairness in Recommendation Ranking through Pairwise Comparisons. KDD 2019: 2212-2220 - [c33]Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi:
Top-K Off-Policy Correction for a REINFORCE Recommender System. WSDM 2019: 456-464 - [c32]Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi:
Towards Neural Mixture Recommender for Long Range Dependent User Sequences. WWW 2019: 1782-1793 - [i16]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi:
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. CoRR abs/1901.04562 (2019) - [i15]Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi:
Towards Neural Mixture Recommender for Long Range Dependent User Sequences. CoRR abs/1902.08588 (2019) - [i14]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow:
Fairness in Recommendation Ranking through Pairwise Comparisons. CoRR abs/1903.00780 (2019) - [i13]Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi:
Transfer of Machine Learning Fairness across Domains. CoRR abs/1906.09688 (2019) - [i12]Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel:
Toward a better trade-off between performance and fairness with kernel-based distribution matching. CoRR abs/1910.11779 (2019) - [i11]Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems. CoRR abs/1911.01916 (2019) - 2018
- [c31]Francois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi:
Factorized Recurrent Neural Architectures for Longer Range Dependence. AISTATS 2018: 1522-1530 - [c30]Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, Ed H. Chi:
Q&R: A Two-Stage Approach toward Interactive Recommendation. KDD 2018: 139-148 - [c29]Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi:
Categorical-attributes-based item classification for recommender systems. RecSys 2018: 320-328 - [c28]Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis:
The Case for Learned Index Structures. SIGMOD Conference 2018: 489-504 - [c27]Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, Ed H. Chi:
Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM 2018: 46-54 - [i10]Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel:
Counterfactual Fairness in Text Classification through Robustness. CoRR abs/1809.10610 (2018) - [i9]Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi:
Top-K Off-Policy Correction for a REINFORCE Recommender System. CoRR abs/1812.02353 (2018) - 2017
- [c23]Alex Beutel, Ed Huai-hsin Chi, Zhiyuan Cheng, Hubert Pham, John R. Anderson:
Beyond Globally Optimal: Focused Learning for Improved Recommendations. WWW 2017: 203-212 - [i7]Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi:
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. CoRR abs/1707.00075 (2017) - [i6]Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis:
The Case for Learned Index Structures. CoRR abs/1712.01208 (2017)
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